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https://github.com/wassname/scikit-image.git
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Added stronger tests
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@@ -2,6 +2,7 @@ import warnings
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import numpy as np
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from numpy.testing import assert_array_almost_equal as assert_close
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from numpy.testing import assert_array_equal
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import skimage
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from skimage import data
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from skimage import exposure
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@@ -184,8 +185,7 @@ def test_rescale_intensity_gamma_one():
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"""Same image should be returned for gamma equal to one"""
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image = data.camera()
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result = exposure.rescale_intensity_gamma(image, 1)
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assert result.mean() == image.mean()
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assert result.std() == image.std()
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assert_array_equal(result, image)
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def test_rescale_intensity_gamma_zero():
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@@ -198,72 +198,125 @@ def test_rescale_intensity_gamma_zero():
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def test_rescale_intensity_gamma_less_one():
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"""Output's mean should be greater than input's mean for gamma less than
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one"""
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image = data.camera()
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"""Verifying the output with expected results for gamma
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correction with gamma equal to half"""
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image = np.uint8(4 * np.arange(64).reshape(8,8))
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expected = np.array([[ 0, 31, 45, 55, 63, 71, 78, 84],
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[ 90, 95, 100, 105, 110, 115, 119, 123],
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[127, 131, 135, 139, 142, 146, 149, 153],
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[156, 159, 162, 165, 168, 171, 174, 177],
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[180, 183, 186, 188, 191, 194, 196, 199],
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[201, 204, 206, 209, 211, 214, 216, 218],
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[221, 223, 225, 228, 230, 232, 234, 236],
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[238, 241, 243, 245, 247, 249, 251, 253]], dtype=np.uint8)
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result = exposure.rescale_intensity_gamma(image, 0.5)
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assert result.mean() > image.mean()
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assert_array_equal(result, expected)
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def test_rescale_intensity_gamma_greater_one():
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"""Output's mean should be less than input's mean for gamma greater than
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one"""
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image = data.camera()
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"""Verifying the output with expected results for gamma
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correction with gamma equal to two"""
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image = np.uint8(4 * np.arange(64).reshape(8,8))
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expected = np.array([[ 0, 0, 0, 0, 1, 1, 2, 3],
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[ 4, 5, 6, 7, 9, 10, 12, 14],
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[ 16, 18, 20, 22, 25, 27, 30, 33],
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[ 36, 39, 42, 45, 49, 52, 56, 60],
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[ 64, 68, 72, 76, 81, 85, 90, 95],
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[100, 105, 110, 116, 121, 127, 132, 138],
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[144, 150, 156, 163, 169, 176, 182, 189],
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[196, 203, 211, 218, 225, 233, 241, 249]], dtype=np.uint8)
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result = exposure.rescale_intensity_gamma(image, 2)
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assert result.mean() < image.mean()
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assert_array_equal(result, expected)
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# Test Logarithmic Correction
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# ===========================
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def test_rescale_intensity_logarithmic():
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"""Output's mean should be greater than input's mean and all pixel values
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in output should be either greater than or equal to that of corresponding
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pixel in input for logarithmiccorrection with multiplier constant equal
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to unity"""
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image = data.camera()
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"""Verifying the output with expected results for logarithmic
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correction with multiplier constant multiplier equal to unity"""
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image = np.uint8(4 * np.arange(64).reshape(8,8))
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expected = np.array([[ 0, 5, 11, 16, 22, 27, 33, 38],
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[ 43, 48, 53, 58, 63, 68, 73, 77],
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[ 82, 86, 91, 95, 100, 104, 109, 113],
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[117, 121, 125, 129, 133, 137, 141, 145],
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[149, 153, 157, 160, 164, 168, 172, 175],
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[179, 182, 186, 189, 193, 196, 199, 203],
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[206, 209, 213, 216, 219, 222, 225, 228],
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[231, 234, 238, 241, 244, 246, 249, 252]], dtype=np.uint8)
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result = exposure.rescale_intensity_logarithmic(image, 1)
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assert result.mean() > image.mean()
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assert result.all() >= image.all()
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assert_array_equal(result, expected)
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def test_rescale_intensity_inv_logarithmic():
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"""Output's mean should be less than input's mean and all pixel values
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in output should be either less than or equal to that of corresponding
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pixel in inputfor inverse logarithmic correction with multiplier constant
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equal to unity"""
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image = data.camera()
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"""Verifying the output with expected results for inverse logarithmic
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correction with multiplier constant multiplier equal to unity"""
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image = np.uint8(4 * np.arange(64).reshape(8,8))
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expected = np.array([[ 0, 2, 5, 8, 11, 14, 17, 20],
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[ 23, 26, 29, 32, 35, 38, 41, 45],
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[ 48, 51, 55, 58, 61, 65, 68, 72],
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[ 76, 79, 83, 87, 90, 94, 98, 102],
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[106, 110, 114, 118, 122, 126, 130, 134],
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[138, 143, 147, 151, 156, 160, 165, 170],
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[174, 179, 184, 188, 193, 198, 203, 208],
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[213, 218, 224, 229, 234, 239, 245, 250]], dtype=np.uint8)
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result = exposure.rescale_intensity_logarithmic(image, 1, -1)
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assert result.mean() < image.mean()
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assert result.all() <= image.all()
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assert_array_equal(result, expected)
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# Test Sigmoid Correction
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# =======================
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def test_rescale_intensity_sigmoid_cutoff_one():
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"""Output's std should be less than input's std and all pixel values
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in output should be either less than or equal to that of
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corresponding pixel in input for sigmoid correction with cutoff equal
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to one and gain of 10"""
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image = data.camera()
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result = exposure.rescale_intensity_sigmoid(image, 1, 10)
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assert result.std() < image.std()
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assert result.all() <= image.all()
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"""Verifying the output with expected results for sigmoid correction
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with cutoff equal to one and gain of 5"""
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image = np.uint8(4 * np.arange(64).reshape(8,8))
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expected = np.array([[ 1, 1, 1, 2, 2, 2, 2, 2],
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[ 3, 3, 3, 4, 4, 4, 5, 5],
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[ 5, 6, 6, 7, 7, 8, 9, 10],
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[ 10, 11, 12, 13, 14, 15, 16, 18],
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[ 19, 20, 22, 24, 25, 27, 29, 32],
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[ 34, 36, 39, 41, 44, 47, 50, 54],
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[ 57, 61, 64, 68, 72, 76, 80, 85],
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[ 89, 94, 99, 104, 108, 113, 118, 123]], dtype=np.uint8)
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result = exposure.rescale_intensity_sigmoid(image, 1, 5)
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assert_array_equal(result, expected)
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def test_rescale_intensity_sigmoid_cutoff_zero():
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"""Output's std should be less than input's std and all pixel values
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in output should be either greater than or equal to that of
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corresponding pixel in input for sigmoid correction with cutoff equal
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to zero and gain of 10"""
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image = data.camera()
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"""Verifying the output with expected results for sigmoid correction
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with cutoff equal to zero and gain of 10"""
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image = np.uint8(4 * np.arange(64).reshape(8,8))
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expected = np.array([[127, 137, 147, 156, 166, 175, 183, 191],
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[198, 205, 211, 216, 221, 225, 229, 232],
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[235, 238, 240, 242, 244, 245, 247, 248],
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[249, 250, 250, 251, 251, 252, 252, 253],
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[253, 253, 253, 253, 254, 254, 254, 254],
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[254, 254, 254, 254, 254, 254, 254, 254],
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[254, 254, 254, 254, 254, 254, 254, 254],
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[254, 254, 254, 254, 254, 254, 254, 254]], dtype=np.uint8)
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result = exposure.rescale_intensity_sigmoid(image, 0, 10)
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assert result.std() < image.std()
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assert result.all() >= image.all()
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assert_array_equal(result, expected)
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def test_rescale_intensity_sigmoid_cutoff_half():
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"""Output's std should be greater than input's std for sigmoid
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correction with cutoff equal to 0.5 and gain of 10"""
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image = data.camera()
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"""Verifying the output with expected results for sigmoid correction
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with cutoff equal to half and gain of 10"""
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image = np.uint8(4 * np.arange(64).reshape(8,8))
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expected = np.array([[ 1, 1, 2, 2, 3, 3, 4, 5],
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[ 5, 6, 7, 9, 10, 12, 14, 16],
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[ 19, 22, 25, 29, 34, 39, 44, 50],
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[ 57, 64, 72, 80, 89, 99, 108, 118],
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[128, 138, 148, 158, 167, 176, 184, 192],
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[199, 205, 211, 217, 221, 226, 229, 233],
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[236, 238, 240, 242, 244, 246, 247, 248],
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[249, 250, 250, 251, 251, 252, 252, 253]], dtype=np.uint8)
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result = exposure.rescale_intensity_sigmoid(image, 0.5, 10)
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assert result.std() > image.std()
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assert_array_equal(result, expected)
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